# MMPD: Multi-Domain Mobile Video Physiology Dataset arXiv: https://arxiv.org/abs/2302.03840 ยท HTML: https://mlsanity.com/paper/2302.03840/ ## Key facts - Cited by 57 papers in the map (data through 14 Oct 2026). - Builds on 7 of its 23 references that are in the map. - First posted on arXiv 8 Feb 2023. ## Builds on (most cited first, 7 of 7) - [Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement](https://mlsanity.com/paper/2006.03790/) (2020) - [VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video](https://mlsanity.com/paper/1810.04927/) (2018) - [A Reproducible Study on Remote Heart Rate Measurement](https://mlsanity.com/paper/1709.00962/) (2017) - [Camera Measurement of Physiological Vital Signs](https://mlsanity.com/paper/2111.11547/) (2021) - [SCAMPS: Synthetics for Camera Measurement of Physiological Signals](https://mlsanity.com/paper/2206.04197/) (2022) - [Real-time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation](https://mlsanity.com/paper/2012.15846/) (2020) - [MobilePhys: Personalized Mobile Camera-Based Contactless Physiological Sensing](https://mlsanity.com/paper/2201.04039/) (2022) ## Built on by (most cited first, 30 of 57) - [RhythmFormer: Extracting Patterned rPPG Signals based on Periodic Sparse Attention](https://mlsanity.com/paper/2402.12788/) (2024) - [RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement](https://mlsanity.com/paper/2404.06483/) (2024) - [Spiking-PhysFormer: Camera-Based Remote Photoplethysmography with Parallel Spike-driven Transformer](https://mlsanity.com/paper/2402.04798/) (2024) - [PhysMamba: Efficient Remote Physiological Measurement with SlowFast Temporal Difference Mamba](https://mlsanity.com/paper/2409.12031/) (2024) - [Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge](https://mlsanity.com/paper/2403.06947/) (2024) - [Motion Matters: Neural Motion Transfer for Better Camera Physiological Measurement](https://mlsanity.com/paper/2303.12059/) (2023) - [egoPPG: Heart Rate Estimation from Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks](https://mlsanity.com/paper/2502.20879/) (2025) - [PhysMLE: Generalizable and Priors-Inclusive Multi-task Remote Physiological Measurement](https://mlsanity.com/paper/2405.06201/) (2024) - [PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement](https://mlsanity.com/paper/2509.24850/) (2025) - [Camera-Based HRV Prediction for Remote Learning Environments](https://mlsanity.com/paper/2305.04161/) (2023) - [PhysDrive: A Multimodal Remote Physiological Measurement Dataset for In-vehicle Driver Monitoring](https://mlsanity.com/paper/2507.19172/) (2025) - [Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation](https://mlsanity.com/paper/2404.09378/) (2024) - [FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography](https://mlsanity.com/paper/2604.00534/) (2026) - [MSPM: A Multi-Site Physiological Monitoring Dataset for Remote Pulse, Respiration, and Blood Pressure Estimation](https://mlsanity.com/paper/2402.02224/) (2024) - [SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency](https://mlsanity.com/paper/2409.12040/) (2024) - [Toward Motion Robustness: A masked attention regularization framework in remote photoplethysmography](https://mlsanity.com/paper/2407.06653/) (2024) - [PhysFlow: Skin tone transfer for remote heart rate estimation through conditional normalizing flows](https://mlsanity.com/paper/2407.21519/) (2024) - [Non-Contact Health Monitoring During Daily Personal Care Routines](https://mlsanity.com/paper/2506.09718/) (2025) - [Reperio-rPPG: Relational Temporal Graph Neural Networks for Periodicity Learning in Remote Physiological Measurement](https://mlsanity.com/paper/2511.05946/) (2025) - [Measuring Domain Shifts using Deep Learning Remote Photoplethysmography Model Similarity](https://mlsanity.com/paper/2404.08184/) (2024) - [Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference](https://mlsanity.com/paper/2407.13974/) (2024) - [PhysMamba: State Space Duality Model for Remote Physiological Measurement](https://mlsanity.com/paper/2408.01077/) (2024) - [Passive Heart Rate Monitoring During Smartphone Use in Everyday Life](https://mlsanity.com/paper/2503.03783/) (2025) - [Robust and Generalizable Heart Rate Estimation via Deep Learning for Remote Photoplethysmography in Complex Scenarios](https://mlsanity.com/paper/2507.07795/) (2025) - [BeatFormer: Efficient motion-robust remote heart rate estimation through unsupervised spectral zoomed attention filters](https://mlsanity.com/paper/2507.14885/) (2025) - [TYrPPG: Uncomplicated and Enhanced Learning Capability rPPG for Remote Heart Rate Estimation](https://mlsanity.com/paper/2511.05833/) (2025) - [PhysNeXt: Next-Generation Dual-Branch Structured Attention Fusion Network for Remote Photoplethysmography Measurement](https://mlsanity.com/paper/2603.19752/) (2026) - [Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG](https://mlsanity.com/paper/2307.12644/) (2023) - [MHAD: Multimodal Home Activity Dataset with Multi-Angle Videos and Synchronized Physiological Signals](https://mlsanity.com/paper/2409.09366/) (2024) - [Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios](https://mlsanity.com/paper/2503.23930/) (2025) ## Abstract (arXiv) Remote photoplethysmography (rPPG) is an attractive method for noninvasive, convenient and concomitant measurement of physiological vital signals. Public benchmark datasets have served a valuable role in the development of this technology and improvements in accuracy over recent years.However, there remain gaps in the public datasets.First, despite the ubiquity of cameras on mobile devices, there are few datasets recorded specifically with mobile phone cameras. Second, most datasets are relatively small and therefore are limited in diversity, both in appearance (e.g., skin tone), behaviors (e.g., motion) and environment (e.g., lighting conditions). In an effort to help the field advance, we present the Multi-domain Mobile Video Physiology Dataset (MMPD), comprising 11 hours of recordings from mobile phones of 33 subjects. The dataset is designed to capture videos with greater representation across skin tone, body motion, and lighting conditions. MMPD is comprehensive with eight descriptive labels and can be used in conjunction with the rPPG-toolbox. The reliability of the dataset is verified by mainstream unsupervised methods and neural methods. The GitHub repository of our dataset: https://github.com/THU-CS-PI/MMPD_rPPG_dataset.